Source: my own notes taken during Standford’s Machine Learning course in Coursera, by Andrew Ng. All credits to him and this organization. The course is freely available for anybody to be taken at their own pace. The images are made by myself using LaTeX (formulas) and R (graphics). Hypothesis function Logistic regression is used when … Read more
Recall literally is how many of the true positives were recalled (found), i.e. how many of the correct hits were also found. Precision (your formula is incorrect) is how many of the returned hits were true positive i.e. how many of the found were correct hits.
GANs are unsupervised learning algorithms that use a supervised loss as part of the training. The later appears to be where you are getting hung-up. When we talk about supervised learning, we are usually talking about learning to predict a label associated with the data. The goal is for the model to generalize to new … Read more
Octave 4.0.0 doesn’t work well with submit scripts (on Ubuntu). Check the version on your terminal: octave –version if it’s 4.0.0 update it. sudo add-apt-repository ppa:octave/stable sudo apt update sudo apt-get install octave There is also warning in the discussions.
Let’s use the example of classifying the gender of a person. Your understanding about class is correct! Given an input observation, our Naive Bayes Classifier should output a category. The class is that category. Features: Features in a Naive Bayes Classifier, or any general ML Classification Algorithm, are the data points we choose to define … Read more
A little bit of theory first, sorry! You asked for the grad and hessian for MAE, however, the MAE is not continuously twice differentiable so trying to calculate the first and second derivatives becomes tricky. Below we can see the “kink” at x=0 which prevents the MAE from being continuously differentiable. Moreover, the second derivative … Read more
It is a very good way to increase the number of date you have. What you’ll do depends on your data. For example, if you are training on data obtained from a sensor, you may want to add some noise to the training data so that you can increase your dataset. After all, you can … Read more
My first thought is to try to parse the names into a description of features (company LG, size 42 Inch, resolution 1080p, type LCD HDTV). Then you can match these descriptions against each other for compatibility; it’s okay to omit a product number but bad to have different sizes. Simple are-the-common-attributes-compatible might be enough, or … Read more
[Answer after the question was edited & clarified:] Before rushing into implementation issues, it is always a good practice to take some time to think about the methodology and the task itself; arguably, intermingling early stopping with the cross validation procedure is not a good idea. Let’s make up an example to highlight the argument. … Read more
In Deep Neural Networks the depth refers to how deep the network is but in this context, the depth is used for visual recognition and it translates to the 3rd dimension of an image. In this case you have an image, and the size of this input is 32x32x3 which is (width, height, depth). The … Read more